A tailored course, built for your situation
Mastering ISO 27001 for AI/ML Engineering Managers
A structured path to full command of information security frameworks in AI-driven environments
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Audit evidence packages that require last-minute rework under regulator cycles
The situation this course is for
Engineering teams waste cycles rebuilding audit trails because control documentation lacks clarity, traceability, or framework precision, especially when AI systems are in scope.
Who this is for
Senior technical manager in a global systems integrator, accountable for on-time, compliant AI/ML delivery under ISO-aligned governance
Who this is not for
Entry-level engineers, non-technical compliance staff, or consultants without hands-on implementation experience
What you walk away with
- Produce complete, regulator-ready SoAs without cross-team chasing
- Map AI/ML system controls directly to ISO 27001 clauses with zero rework
- Automate control evidence collection for recurring audits
- Standardize security narratives across client engagements
- Reduce audit preparation time by 90% using reusable templates
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27001 in AI-Driven Environments
Understand how ISO 27001 applies uniquely to machine learning systems, data pipelines, and model deployment workflows.
12 chapters in this module
- Why ISO 27001 matters more for AI than traditional IT systems
- Mapping AI system boundaries to information security domains
- Defining asset ownership in shared cloud environments
- Classifying data types in ML training and inference
- Risk assessment inputs specific to AI components
- Integrating threat modeling into model development lifecycle
- Establishing security roles in cross-functional AI teams
- Documenting governance structure for compliance audits
- Setting scope statements that withstand regulator scrutiny
- Avoiding common exclusions that trigger findings
- Linking AI model cards to security policy documentation
- Creating audit-ready definitions of 'information asset'
Module 2. Building a Compliant AI System Boundary
Learn how to define and document the secure perimeter of an AI/ML system for ISO 27001 alignment.
12 chapters in this module
- Identifying entry and exit points in model inference APIs
- Tracing data flow across training, validation, and serving
- Documenting third-party dependencies in model supply chain
- Isolating development, staging, and production environments
- Accounting for data labeling and annotation workflows
- Securing access to model repositories and artifacts
- Handling synthetic data within the security boundary
- Managing features stored in offline and online stores
- Defining custody transfers between data engineering and MLOps
- Auditing boundary changes during retraining cycles
- Versioning system architecture diagrams for compliance
- Producing diagrams that satisfy auditor traceability needs
Module 3. Risk Assessment for Machine Learning Systems
Apply ISO 27001 risk methodology to AI-specific threats like data poisoning, model inversion, and unintended bias.
12 chapters in this module
- Adapting risk criteria for probabilistic AI outputs
- Identifying threat actors targeting model inference APIs
- Assessing impact of model drift on security posture
- Evaluating data leakage risks in transfer learning
- Scoring likelihood of adversarial attacks on models
- Linking fairness assessments to security risk registers
- Using SHAP values to justify control investments
- Documenting risk treatment plans for high-scoring items
- Integrating model monitoring alerts into risk dashboards
- Aligning risk appetite statements with client SLAs
- Reporting residual risk to compliance stakeholders
- Updating risk assessments after model version updates
Module 4. Control Mapping for AI Development Lifecycle
Map ISO 27001 controls to specific phases of the AI/ML engineering process.
12 chapters in this module
- Applying A.8.1 asset management controls to datasets
- Enforcing access control (A.9) on model training jobs
- Implementing encryption (A.10) for model weights and artifacts
- Securing logging (A.12) in distributed training clusters
- Applying change management (A.14) to model pipelines
- Ensuring test data integrity (A.8.2) in validation
- Protecting model cards (A.5.35) as confidential documents
- Auditing model registry access (A.12.4)
- Securing CI/CD pipelines (A.14.2) for MLOps
- Maintaining version control (A.14.2.1) for models and code
- Applying supplier risk (A.15) to third-party APIs
- Documenting control mappings in audit-ready format
Module 5. Developing Security-Aware Model Documentation
Create model cards, data cards, and system documentation that satisfy both technical and compliance needs.
12 chapters in this module
- Including security metadata in model card templates
- Documenting data provenance for compliance audits
- Stating model use limitations in enforceable terms
- Recording bias testing methodology and results
- Describing encryption in transit and at rest
- Noting access control mechanisms for model endpoints
- Versioning model documentation alongside code
- Embedding control references in technical specs
- Linking model cards to risk treatment plans
- Using standardized templates across client projects
- Generating documentation automatically from CI/CD
- Tailoring detail level for regulator versus developer audiences
Module 6. Automating Evidence Collection for Audits
Design systems that auto-generate ISO-compliant evidence for controls.
12 chapters in this module
- Configuring logging to capture control-relevant events
- Tagging infrastructure as code with control IDs
- Exporting role assignments from IAM systems
- Automating screenshots of access reviews
- Generating control status dashboards from CI/CD
- Capturing model drift alerts as control evidence
- Storing evidence in time-stamped, tamper-proof format
- Using workflow tools to trigger evidence collection
- Integrating control checks into pull request pipelines
- Validating evidence completeness before audit cycles
- Reducing manual follow-ups with pre-populated forms
- Aligning automation scope with ISO 27001:the current cycle updates
Module 7. Writing Auditor-Ready Policies for AI Systems
Draft policies that satisfy ISO 27001 requirements while reflecting AI engineering realities.
12 chapters in this module
- Defining acceptable use of generative AI in development
- Setting data retention rules for training datasets
- Establishing model deprecation and retirement process
- Writing access control policies for model endpoints
- Documenting incident response for model compromise
- Stating requirements for third-party model auditing
- Enforcing model explainability in production systems
- Requiring bias testing before model deployment
- Mandating encryption for model weights at rest
- Setting retraining frequency based on data drift
- Requiring human-in-the-loop for high-risk decisions
- Linking policy clauses to specific ISO 27001 controls
Module 8. Streamlining Internal Audit Preparation
Turn audit preparation from a quarterly crisis into a continuous process.
12 chapters in this module
- Scheduling control reviews aligned with sprint cycles
- Assigning control ownership to MLOps engineers
- Using checklists tailored to AI system components
- Conducting mock audits with cross-functional teams
- Documenting findings resolution in tracking systems
- Sharing audit status with program management
- Preparing evidence packs before regulator request
- Standardizing responses to common auditor questions
- Building audit timelines into project plans
- Training engineers on auditor communication
- Reducing last-minute escalations with early reviews
- Creating living SoAs updated with each release
Module 9. Integrating ISO 27001 into MLOps Workflows
Embed compliance checks directly into model development and deployment pipelines.
12 chapters in this module
- Adding control validation to CI/CD gates
- Scanning for hardcoded secrets in model code
- Enforcing model signing before deployment
- Validating model card completeness in PR checks
- Running bias tests in automated test suites
- Checking data license compliance in training jobs
- Enforcing encryption standards in artifact storage
- Verifying access logging is enabled on endpoints
- Blocking deployment without risk assessment sign-off
- Integrating compliance gates with Jira workflows
- Alerting on configuration drift from baseline
- Generating compliance reports on every release
Module 10. Managing Third-Party and Supply Chain Risk
Apply ISO 27001 controls to external vendors, APIs, and pre-trained models.
12 chapters in this module
- Assessing security posture of third-party AI APIs
- Reviewing terms of service for model hosting providers
- Auditing data handling practices of labeling vendors
- Evaluating security of open-source model repositories
- Managing risk in transfer learning with public models
- Requiring SOC 2 reports from AI platform providers
- Documenting due diligence for pre-trained embeddings
- Setting access control requirements for API keys
- Monitoring uptime and incident reporting from vendors
- Creating contingency plans for vendor service outages
- Conducting annual reviews of critical suppliers
- Maintaining inventory of third-party components in models
Module 11. Scaling Compliance Across Client Engagements
Reuse compliance components efficiently across multiple projects and industries.
12 chapters in this module
- Creating template control mappings for common patterns
- Developing industry-specific policy addenda
- Standardizing model documentation formats
- Building reusable evidence automation scripts
- Maintaining a library of approved vendor assessments
- Adapting SoA templates for different clients
- Versioning compliance artifacts with project lifecycles
- Training new team members on compliance workflows
- Documenting deviations with justification templates
- Sharing best practices across delivery teams
- Capturing lessons learned from past audits
- Reducing setup time for new engagements
Module 12. Maintaining Certification Over Time
Keep ISO 27001 certification current with minimal disruption to delivery teams.
12 chapters in this module
- Scheduling internal audits throughout the year
- Tracking control effectiveness metrics over time
- Updating risk assessments after incident responses
- Managing documentation changes with version control
- Revalidating controls after infrastructure changes
- Handling certification renewal with auditors
- Reporting compliance status to leadership
- Integrating feedback from external audits
- Updating training materials after framework changes
- Planning resource needs for surveillance audits
- Aligning certification scope with business evolution
- Celebrating compliance milestones with teams
How this maps to your situation
- First-time ISO 27001 implementation in an AI project
- Preparing for external certification audit
- Responding to client-requested compliance evidence
- Scaling compliance practices across multiple AI deliveries
Before vs. after
Before
Spending weeks assembling audit evidence, rewriting policies, and chasing engineers for documentation during compliance cycles.
After
Producing regulator-ready SoAs and control mappings in hours, with automated evidence and standardized templates.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: 90 minutes per week over 12 weeks, or accelerated completion in one weekend for time-constrained practitioners.
If nothing changes
Without structured ISO 27001 implementation, AI projects face delayed sign-offs, repeated audit findings, and increased delivery risk , especially under growing client scrutiny.
How this compares to the alternatives
Unlike generic compliance courses, this program focuses specifically on AI/ML engineering contexts, with real-world templates and automation strategies not found in standard training.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if my client uses SOC 2 instead of ISO 27001?
Yes , the control concepts are transferable, and the course includes mapping guidance between frameworks.
Will this help with ISO 42001 for AI systems?
Yes , the foundational control understanding applies directly, with additional focus on data governance and transparency.
$199 one-time. 90 minutes per week over 12 weeks, or accelerated completion in one weekend for time-constrained practitioners..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours